import pandas as pd
import seaborn as sns
import plotly.express as px

import matplotlib.pyplot as plt
from matplotlib.pyplot import figure

import plotly.io as pio
pio.renderers.default = "plotly_mimetype+notebook"

Matplotlib

For this excercise, we have written the following code to load the stock dataset built into plotly express.

stocks = px.data.stocks()
stocks.head()

	date	GOOG	AAPL	AMZN	FB	NFLX	MSFT
0	2018-01-01	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	2018-01-08	1.018172	1.011943	1.061881	0.959968	1.053526	1.015988
2	2018-01-15	1.032008	1.019771	1.053240	0.970243	1.049860	1.020524
3	2018-01-22	1.066783	0.980057	1.140676	1.016858	1.307681	1.066561
4	2018-01-29	1.008773	0.917143	1.163374	1.018357	1.273537	1.040708

Question 1:

Select a stock and create a suitable plot for it. Make sure the plot is readable with relevant information, such as date, values.

fig, ax = plt.subplots()
ax.plot(stocks.loc[:,'date'],stocks.loc[:,'GOOG'])
# set title
ax.set_title('Google stock')
# horizontal axis
ax.set_xlabel('Date')
ax.set_xticks(stocks.loc[::10,'date'])
# vertical axis
ax.set_ylabel('Stock value')
fig.set_size_inches(18.5, 10.5)
plt.show()

Question 2:

You've already plot data from one stock. It is possible to plot multiples of them to support comparison.
To highlight different lines, customise line styles, markers, colors and include a legend to the plot.

fig, ax = plt.subplots()
ax.plot(stocks.loc[:,'date'],stocks.loc[:,'GOOG'],label = "GOOG")
ax.plot(stocks.loc[:,'date'],stocks.loc[:,'AAPL'],label = "AAPL")
ax.plot(stocks.loc[:,'date'],stocks.loc[:,'AMZN'],label = "AMZN")
ax.plot(stocks.loc[:,'date'],stocks.loc[:,'FB'],label = "FB")
ax.plot(stocks.loc[:,'date'],stocks.loc[:,'NFLX'],label = "NFLX")
ax.plot(stocks.loc[:,'date'],stocks.loc[:,'MSFT'],label = "MSFT")
# set title
ax.set_title('Stocks')
# horizontal axis
ax.set_xlabel('Date')
ax.set_xticks(stocks.loc[::10,'date'])
# vertical axis
ax.set_ylabel('Stock value')
ax.legend()
fig.set_size_inches(18.5, 10.5)
plt.show()

Seaborn

First, load the tips dataset

tips = sns.load_dataset('tips')
tips.head()

	total_bill	tip	sex	smoker	day	time	size
0	16.99	1.01	Female	No	Sun	Dinner	2
1	10.34	1.66	Male	No	Sun	Dinner	3
2	21.01	3.50	Male	No	Sun	Dinner	3
3	23.68	3.31	Male	No	Sun	Dinner	2
4	24.59	3.61	Female	No	Sun	Dinner	4

Question 3:

Let's explore this dataset. Pose a question and create a plot that support drawing answers for your question.

Some possible questions:

• Are there differences between male and female when it comes to giving tips?
• What attribute correlate the most with tip?

My question

Is there relation between day of the week and the amout of total tips sum for the day? And if yes what day is the most profitable?

# YOUR CODE HERE
g = sns.FacetGrid(tips, col='day')
g.map(sns.scatterplot, 'total_bill', 'tip')
g.add_legend()

fig = px.bar(tips, x="tip", y="day", orientation='h')
plt.show()
fig.show()

Plotly Express

Question 4:

Redo the above exercises (challenges 2 & 3) with plotly express. Create diagrams which you can interact with.

The stocks dataset

Hints:

• Turn stocks dataframe into a structure that can be picked up easily with plotly express

# YOUR CODE HERE
pd.options.plotting.backend = "plotly"
fig = px.line(stocks, stocks.loc[:,'date'], y=[stocks.loc[:,'GOOG'], stocks.loc[:,'AAPL'],\
                                               stocks.loc[:,'AMZN'],stocks.loc[:,'FB'],stocks.loc[:,'NFLX'],stocks.loc[:,'MSFT']])
fig.show()

The tips dataset

fig1=px.scatter(tips, x="total_bill", y="tip", color="day")
fig1.show()

fig2 = px.bar(tips, x="tip", y="day", orientation='h')

fig2.show()

Question 5:

Recreate the barplot below that shows the population of different continents for the year 2007.

Hints:

• Extract the 2007 year data from the dataframe. You have to process the data accordingly
• use plotly bar
• Add different colors for different continents
• Sort the order of the continent for the visualisation. Use axis layout setting
• Add text to each bar that represents the population

#load data
df = px.data.gapminder()
df = df.loc[df['year'] == 2007]
df = df.groupby('continent').sum()
fig1 = px.bar(df, x="pop", y=df.index, orientation='h', color=["orange", "red","green", "blue","purple"], text_auto='.2s')
fig1.update_layout(barmode='stack', yaxis={'categoryorder':'total descending'})

fig1.show()